Tracking Experiments with MLflow

Summary

This course shows you how to design an MLflow experiment to identify the best machine model for deployment.

Description

In this course, we’ll show you how to design an MLflow experiment to identify the best machine model for deployment. 

 

This course is the second in a series of three courses developed to show you how to use Databricks to work with a single data set from experimentation to production-scale machine learning model deployment. The other courses in this series include: 

  • Data Science on Databricks: The Bias-Variance Tradeoff

  • Deploying a Machine Learning Project with MLflow Projects

Learning objectives

  • Create and explore an augmented sample from user event and profile data.

  • Design an MLflow experiment and write notebook-based software to run the experiment to assess various linear models. 

  • Examine experimental results to decide which model to develop for production.

Prerequisites

  • Beginning-level experience running data science workflows in the Databricks Workspace

  • Beginner-level experience with Apache Spark

  • Intermediate-level experience with the Scipy Numerical Stack